Various types of medical imaging systems or modalities are available for generating images of a patient's anatomy and function for diagnostic and treatment purposes. These include X-ray computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”), positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”). These imaging modalities create digital images comprised of an array of numerical values representative of a property (such as a grey scale value) associated with an anatomic location. In two-dimensional (“2-D”) digital images, or slice sections, the discrete array locations are termed pixels. Three-dimensional (“3-D”) digital images are constructed from stacked slice sections through various construction techniques known in the art. In 3-D digital images, the discrete volume elements are termed voxels.
Various analytical approaches can be applied to process digital images to detect, identify, display or highlight regions of interest (“ROI”). For example, digitized images can be processed through segmentation and registration. Segmentation generally involves separating irrelevant objects, or extracting anatomic surfaces, structures, or regions of interest from images for purposes of anatomic identification, diagnosis, evaluation, and volumetric measurement. Image registration is a process of finding correspondence of points in two different images for facilitating comparisons and medical diagnosis.
Conventional segmentation and registration techniques require prior anatomic or geometric knowledge about the image content in order to work reliably. The prior knowledge is either given implicitly or through user interaction. For instance, some prior techniques are limited to segment a given structure such as a certain body region or specific organ, relying on the fact that the image contains the structure to be segmented. In many segmentation techniques, prior knowledge such as guidance points are provided by a computer user through a graphical user interface or formal description.
Conventional segmentation and registration techniques requiring prior knowledge are not sufficiently robust. For example, there is significant probability of mismatch. They also take long computation time and are not satisfactory in dealing with great variability present in daily clinical diagnostic images.